刀具磨损在线监测研究现状与发展

金属加工过程中,切削刀具的状态对于生产效率和表面加工质量有重要影响,因此刀具磨损在线监测具有重要意义。介绍了最近几年常用的刀具磨损在线监测方法,包括切削力、振动、声发射、温度、电流与功率信号,分析了每种信号的监测方法及其特点,并与实验结果进行了对比。提出了多传感器融合技术能有效避免单独使用一种检测手段的弊端。     

TB9钛合金车削系统振动特性与表面粗糙度关系的试验研究

目的 通过车削加工TB9钛合金试验,定量研究不同位置的振动特性对表面粗糙度的影响规律,并建立基于振动参数的表面粗糙度预测模型。方法 选用涂层硬质合金刀具对TB9钛合金线材进行车削加工。通过8704B25和3333A2加速度传感器对试验过程中不同位置的切削振动进行检测。运用Matlab对振动加速度信号进行处理和分析。采用TR2000高精度表面粗糙度仪测量工件表面粗糙度。结果 车削系统不同位置的振动特性均与表面粗糙度存在线性关系。车削系统中刀具振动加速度均方根值、主轴振动加速度均方根值以及后导向振动加速度均方根值与表面粗糙度的Pearson相关系数分别为0.379 93、0.331 90、0.181 95。表面粗糙度预测模型的预测平均百分比误差小于3%。结论 车削加工时刀具、主轴以及后导向的车削振动均对表面粗糙度有一定影响。车削系统不同位置的振动特性对表面粗糙度的影响次序为刀具>主轴>后导向,可见距离切削位置越近的振动对车削加工表面粗糙度的影响越大。基于振动参数的表面粗糙度预测模型的准确度较高,可作为表面粗糙度的预测模型。     

面向绿色高表面质量制造的硬态车削工艺多目标参数优化

目的 为了进行硬态车削绿色制造与工艺性能协同优化研究,提出一种同时考虑碳排放量和表面粗糙度的多目标优化方法。方法 首先,通过分析硬态车削过程中切削参数、工件材料、刀具材料等因素对切削功率的影响建立碳排放目标函数,针对工件的表面粗糙度受到切削条件、工件材料、刀具材料等诸多因素的影响,利用正交试验和广义回归神经网络建立轴承硬态车削表面粗糙度目标函数。然后,考虑加工过程中机床特性和硬车实际工况等约束条件,建立以切削参数为优化变量,以碳排放量和表面粗糙度为优化目标的多目标优化模型,引入权重系数将其转化为单目标优化模型。最后,利用遗传算法对优化模型进行优化求解,深入分析切削参数对优化目标的影响。结果 在工厂实际轴承产品硬车试验中验证了优化模型的有效性,结果表明,切削速度为225 m/min、进给量为0.08 mm/r、背吃刀量为0.10 mm时,碳排放量和表面粗糙度的综合优化指标最低。相比优化前,虽然碳排放量上升了13.05%,但表面质量提升了34.44%。结论 研究结果对面向绿色制造的轴承硬车工艺参数优化提供理论方法有重要意义。     

考虑能耗的多传感器融合加工表面粗糙度预测方法

目的提出一种考虑能耗的多传感器融合加工表面粗糙度预测方法,精确预测零件表面粗糙度。方法首先采集车削过程中的功率和振动信号,测量加工表面粗糙度值,利用集成经验模态分解(Ensemble empirical mode decomposition,EEMD)和小波包分析提取振动信号的时域与频域特征,联合功率信号的时域特征、能耗特征与切削参数,构造联合多特征向量。然后采用核主成分分析(Kernel principal component analysis,KPCA)对联合多特征向量进行融合降维处理生成融合特征。最后将融合特征作为基于支持向量机(Support vector machine,SVM)的表面粗糙度预测模型的输入特征,并使用遗传算法(Genetic algorithm,GA)对SVM模型相关核参数进行优化以提高预测精度。结果预测得到的表面粗糙度平均相对误差为4.91%,最大误差为0.111μm,预测时间为9.24 s。与单传感器预测方法及多传感器联合特征预测方法相比,多传感器融合预测方法具有最高的准确率且预测速度快。结论多传感器采集的信息更全面、准确,保证了预测的准确性,对特征进行融合可进一步提高预测精度。     

涂层硬质合金刀片车削典型CFRP的工程技术研究

用相同几何结构、不同材质的2种涂层硬质合金刀片车削碳纤维增强复合材料T800H棒料,通过多分量力学传感器、压力传感器和高速摄影机监测车削过程中的切削载荷和刀具工作状态,通过光学扫描系统和数码显微系统观测分析工件已加工表面和切屑的形貌特征。结果表明:车削T800H棒料时,每种刀具都存在一个临界切削速度v_cr,在相同进给量f和恒定切削深度a_p的情况下,切削速度v_c对工件已加工表面质量影响小。实验还表明:采用硬度更高的刀具车削碳纤维,能获得更好的表面质量。      

断续磨削表面烧伤机理与在线监测方法研究

目的 研究断续磨削烧伤机理和声发射在线监测方法,避免产品磨削加工烧伤现象.方法 基于平面磨削温度场理论和镜像热源方法,建立一种断续磨削工件边缘的温度场模型,基于该模型可对断续磨削烧伤机理进行研究.为验证上述模型的有效性,通过正交实验设计不同断续磨削工况实验,利用红外热成像仪和声发射信号对断续磨削区温度进行在线监测,使用酸洗法和巴克豪森噪声检测仪对磨削后工件表面进行烧伤检测验证,通过对声发射信号的小波包能量求解,建立其与磨削区温度之间的关系.结果 该模型可有效反映断续磨削时工件边缘处磨削区温度场分布情况.计算结果表明,断续磨削工件断口边缘比其他位置磨削区温度更高,且更容易引起烧伤.实验表明,声发射信号的小波包变换总能量与磨削区呈一定相关性,基于声发射信号可对断续磨削烧伤实施在线监测.结论 实验结果证明了该模型对断续磨削烧伤机理分析的有效性,以及利用声发射信号对断续磨削烧伤在线监测的可行性.最后针对某一转向螺母产品实际断续磨削加工烧伤进行在线监测应用,实践结果表明,该方法比传统酸洗烧伤检测更加高效环保,对实现磨削加工烧伤检测自动化和智能化具有重要意义.     

二维超声振动车削6061铝合金圆筒表面粗糙度研究

目的 研究分析二维超声振动车削加工中切削参数和声学参数对6061铝合金圆筒表面粗糙度的影响。方法 结合二维超声振动特性,建立二维超声振动车削表面粗糙度理论模型,采用四因素四水平正交试验,获得二维超声振动车削6061铝合金圆筒过程中切削参数和声学参数对工件表面粗糙度的影响规律,选取其中4组进行二维超声振动车削与普通车削对比实验,并通过白光干涉仪和超景深显微镜对加工后的工件表面进行观测。结果 正交试验结果表明,切深对加工表面粗糙度的影响不明显,超声振幅、转速、进给量对加工表面粗糙度的影响程度分别为84.35%、11.36%、4.29%。超声和无超声对比实验表明,二维超声振动车削相较于普通车削能显著降低车削表面的粗糙度,最大下降率为47.65%,最小下降率为11.27%;相比于普通车削加工,二维超声振动车削表面具有均匀分布的鱼鳞状微织构。结论 加工参数对表面粗糙度影响的显著从高到低为超声振幅>转速>进给量>切深,最优加工参数为f_r=0.15 mm/r、n=400 r/min、A=2μm、a_p=0.2 mm。采用二维超声振动车削的加...     

关于虚拟车削加工中的粗糙度预测的研究

针对虚拟车削加工中振动问题的处理,对外圆车削加工的理论粗糙度模型进行了修正.通过预测工件与刀具的相对位移,实现了对加工工件的表面形貌的估算.通过对表面形貌的仿真,提高了虚拟车削软件的应用性.     

基于Deform-3D的65Mn硬态切削加工仿真研究

以淬硬钢65Mn为原料,采用有限元分析软件Deform?3D构建了硬态切削加工模型。模拟了不同切削速度下淬硬钢65Mn的车削加工过程,对车削加工的切削力、温度随切削速度的提高的变化以及工件表面应力变化的仿真结果给出了分析,为实现工艺参数的优化提供了指导。结果表明：硬态切削加工3个切削力中径向力Fy 最大,第二变形区的切削温度较高,硬态切削加工过程中工件表面应力呈现拉压交替变换且最终表现为压应力。     

精密孔加工中声发射信号自动检测刀具对中的新方法

本文介绍在对精密孔进行车削，镗削加工过程中，使用声发射技术自动检测刀具对中的方法及为此建立的刀具准确，灵敏对中的声发射检测实验系统；研究了切削加工中对声发射信号特征参量大小的影响因素，得出了ＡＥ信号参量和刀具与工件孔间偏心量的关系，文章同时介绍了新研制的专用对中仪。     

外圆磨削颤振监测方法设计

磨削颤振是影响加工质量和加工效率的重要因素,常需增加多个后续加工周期来满足加工质量的要求。采用涡流传感器、磨削力传感器、声发射传感器、加速度传感器和接触探头等多传感器对砂轮边沿的自激振荡进行测量,对自激振荡信号,提出采用幅值法和小波分析法进行分析处理,实现了磨削颤振的自动监测和预测,减少了加工周期,提高了加工效率。     

A novel approach for real-time prediction and prevention of tool chipping in intermittent turning machining

An integrated tool condition monitoring system, based on a novel signal processing approach, for online prediction and prevention of tool chipping in intermittent turning is presented. It identifies the unstable crack propagation features of the prefailure phase, independent of the cutting parameters and workpiece material. A correlation between the chipping size and these features was developed for decision making, to protect machined surfaces. Experimental validation results confirmed the accuracy of the proposed system. The time required for signal processing, decision making and communication with the machine controller allows stopping the operation before part damage. No such system is presently available.     

Analysis of acoustic emission in chatter vibration with tool wear effect in turning

The progressive wear of cutting tools and occurrence of chatter vibration often pose limiting factors on the achievable productivity in machining processes. An effective in-process monitoring system for tool wear and chatter therefore offers the unique advantage of relaxing the process parameter constraints and optimizing the machining production rate. This research presents a dynamic model of the cutting RMS acoustic emission (AE) signal when chatter occurs in turning, and it determines how this motion is related to the RMS AE signal in the presence of tool flank wear. The tool wear effect on acoustic emission generated in turning is expressed as an explicit function of the cutting parameters and tool/workpiece geometry. The AE generated from the sliding contact on the flank wear flat during chatter is investigated based on the energy dissipation principle. This model offers an explanation of the phenomenon of chatter vibration in the neighborhood of the chatter frequency of the tool. It also sheds light on the variation of the RMS AE signal power in close correlation to the characteristic of the state of wear. Cutting tests were conducted to determine the amplitude relationship between RMS AE and cutting parameters. It is shown that RMS AE is quite sensitive to the dynamic incremental changes in the friction and the wear flat mechanism active in machining processes.     

硬质合金PA30高速深磨过程中声发射信号特征的变化研究

在硬脆难加工材料硬质合金PA30高速深磨声发射实验中,随着工件速度和磨削切削深度增加,声发射信号特征参数均方根（AE_RMS）和磨削力变大;随着砂轮速度增大,AE_RMS和磨削力减小。磨削力和AE_RMS有相同的变化趋势。硬质合金PA30高速深磨AE频谱的能量在100~600kHz的频段比较集中,其AE信号频谱的能量与频率范围明显高于低速浅切磨削。      

基于钢岔管水压试验中的声发射检测技术应用研究

为保证钢岔管在水压试验过程中能够安全进行,本文采用声发射实时监测技术对钢岔管焊缝和母材产生的声发射信号进行实时监测。在升压之前,用断铅信号模拟源分析了随着传播距离的增加声发射信号衰减的情况,并给出了满足同类型试验的幅值与传播距离的关系式;在升压和保压过程中,综合采用声发射源的平面定位分析法、特征参数分析法、波形分析法对出现有意义的声发射信号时段进行了详细分析,为钢岔管水压试验的安全监测提供了技术支撑。试验结果对同类型钢岔管水压试验声发射安全监测具有一定的参考价值。     

An approach to use an array of three acoustic emission sensors to locate uneven events in machining—Part 1: method and validation

The paper reports on an approach to use the triangulation technique applied to arrays of acoustic emission sensors for the location of uneven events occurring during machining. The generation of some uneven events (e.g. workpiece surface discontinuities, plucking, smearing) in machining can be associated with the release of acoustic emission energy. When more than one cutting edge is in contact with the workpiece (e.g. broaching, milling) it is difficult to associate the burst of acoustic emission signal with the tooth that generated it and therefore to locate, relatively to the workpiece surface, the position of acoustic source. The location of the acoustic source, related to the occurrence of the uneven event, is evaluated by feeding the time delays in detecting the same burst signal by three acoustic sensors placed on the workpiece, into an analytical geometry model. Using numerical methods in Matlab, the analytical solution for the location of acoustic source was found by adding to the system formed by three spherical analytical equations, with the acoustic emission sensors as their centres, the characteristics of the cutting tool path. The methodology has been calibrated using pencil break lead tests to asses the accuracy of the acoustic source location and the requirements of the data acquisition system. ‘Near-orthogonal’ cutting trials were carried out using two different arrays of acoustic emission sensors and the uneven events located relatively to the workpiece surface. It was found that the proposed methodology has potential to locate, at a reasonable accuracy, the acoustic sources that are related to the occurrence of uneven events in machining. This work represents a preliminary investigation to be used for the location of workpiece defects during machining with multiple cutting edge tools.     

基于振动传感器的车削加工表面粗糙度预测

文章通过深入研究车床精车外圆时刀具和工件存在相对振动的情况下,加工工件表面轮廓的形成机理,探索出一种建立表面粗糙度值预测模型的新方法。并结合传感器技术,搭建一个能用于测量振动信号的实验平台,通过比较表面粗糙度的预测值和实测值,证明预测模型有一定的准确度。     

Real-time acoustic emission monitoring for surface damage in hard machining

Hard machining is a competitive finishing process with substantial benefits in manufacturing precision mechanical components. However, hard machining applications have been remaining slow due to the existence of surface damage such as white layer. The process-induced white layer may have detrimental effect on component life. However, the white layer on a machined component surface could be found only after machining. This post-process scenario imposes a great potential danger to subsequent product performance such as fatigue life. Therefore, real-time monitoring of white layer formation during hard machining has significant economical and durability importance. In this study, a real-time acoustic emission (AE) monitoring system was developed to investigate the sensitivity of a broad AE signal parameters including RMS, frequency, amplitude, and count rate to white layer and corresponding surface finish and tool wear. The experimental results show that AE RMS, frequency, and count rate have good correlation with white layer formation and, thus, may be used to monitor surface integrity factors. The findings provide fundamental information to develop practical on-line AE monitoring system for surface integrity in hard machining.